Method and apparatus for improving transmission of data on a bandwidth expanded channel

ABSTRACT

A method and apparatus for providing transmission of data on a channel in a network. For example, the method determines a ratio of a number of channel uses of the channel to a number of source samples, divides a channel bandwidth into a plurality of subbands of equal bandwidth in accordance with the ratio, receives a source sample block, determines a channel input for each of the plurality of subbands from the source sample block in accordance with a hybrid coding scheme, and transmits, for each of the plurality of subbands, the channel input that is determined over the network.

The present disclosure relates generally to data transmission on acommunication network and, more particularly, to a method and apparatusfor improving data transmission on a bandwidth mismatched channel in anetwork, e.g., a wireless network.

BACKGROUND

Digital coding schemes based on source-channel separation principle aresensitive to channel condition. If the channel condition is worse thanwhat a code is designed for, the decoder fails to completely decode acode that has traversed the channel. On the other hand, if the channelcondition is better than what the code is designed for, the code failsto provide any performance improvement. Thus, the digital coding schemesuffers from a threshold effect.

One approach is to design the coding scheme to achieve an optimalperformance for a given channel condition. However, the resulting codeoffers performance improvement either when the channel condition isworse or when the channel condition is better, but not both.

SUMMARY

In one embodiment, the present disclosure provides a method and anapparatus for providing transmission of data on a channel in a network,e.g., a wireless network. For example, the method determines a ratio ofa number of channel uses of the channel to a number of source samples,divides a channel bandwidth into a plurality of subbands of equalbandwidth in accordance with the ratio, receives a source sample block,determines a channel input for each of the plurality of subbands fromthe source sample block in accordance with a hybrid coding scheme, andtransmits, for each of the plurality of subbands, the channel input thatis determined over the network.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an exemplary transmission system related to thepresent disclosure;

FIG. 2 illustrates a flow chart of a method for providing transmissionof data on a channel in a network, wherein a ratio of a number ofchannel uses of the channel to a number of sources samples is an integervalue greater than one;

FIG. 3 illustrates a flowchart of a method for determining of a channelinput for each of a plurality of subbands in accordance with a hybridcoding scheme; and

FIG. 4 illustrates a high level block diagram of a general purposecomputer suitable for use in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure broadly describes a method and apparatus forimproving the transmission of data on a bandwidth mismatched channel ina network, e.g., a wireless network and the like. Although the teachingsof the present disclosure are discussed below in the context of wirelessnetworks, the teaching is not so limited. Namely, the teachings of thepresent disclosure can be applied for other types of networks,wire-based networks, etc.

Digital communication systems have been widely deployed in moderncommunication networks. However, these digital communications systemsare based on Shannon's source-channel separation principle. Digitalcoding schemes based on source-channel separation principle aresensitive to channel conditions. For example, a code may be designed fora specific channel condition. However, if the channel condition is worsethan what the code is designed for, the decoder may fail to completelydecode a code that has traversed the channel. In another scenario, ifthe channel condition is better than what the code is designed for, thecode may fail to provide any performance improvement. Thus, the digitalcoding scheme based on source-channel separation principle suffers froma threshold effect.

In one embodiment, the present disclosure provides a hybrid analog anddigital coding scheme that is capable of improving decoding performancewhen the channel condition is worse and when the channel condition isbetter, simultaneously. The hybrid analog and digital coding scheme isalso referred to as a hybrid coding scheme. Namely, the coding scheme ofthe present disclosure is able to achieve a performance improvement forboth the better and the worse channel conditions, while keeping theperformance optimal for a given target channel condition.

FIG. 1 illustrates an exemplary transmission system 100 related to thepresent disclosure. The transmission system 100 comprises a source 101(broadly a data source), an encoder 103, a transmission channel 105, adecoder 107, and a sink 109 (broadly a data destination). Thetransmission channel 105 traversed the network 110. The network 110comprises a wireless network. It should be noted that each of themodules discussed above in the transmission system 100 may beimplemented in one or more hardware devices, e.g., one or more hardwarecomputing devices having one or more processors and the like.

In one embodiment, the source 101 transmits samples (e.g., broadly datasamples) to the encoder 103. The encoder 103 performs the coding, i.e.,applying a coding to the received samples. The resulting (encoded)output of the encoder is then transmitted to the decoder 107 via thetransmission channel 105. The decoder 107 performs reconstruction of thesource samples and forwards the decoded samples to the sink 109. Itshould be noted that the transmission system 100 may comprise many othernetwork elements (not shown), e.g., transmitters, receivers, basestations, routers, switches, gateways, and the like.

The problem of providing transmission of data on a bandwidth mismatchedchannel may be formulated as a lossy source broadcast problem with twousers. In one embodiment, the source and the additive noise of thechannel are both Gaussian. The measure used to evaluate the performanceof a code is the reconstruction mean squared error. In order to moreclearly illustrate the problem, the source and channel conditions aremathematically defined as follows:

First, the source is a memoryless zero-mean Gaussian source with unitvariance S˜

(0,1). Similarly, the channel is a memoryless channel given by: Y=X+

, wherein X is the channel input subject to an average power constraintP, and

˜

(0, N) is a memoryless additive Gaussian noise. Then, the transmissionsignal to noise ratio (P/N) is denoted by SNR.

In order to clearly illustrate the teachings of the current disclosure,the concept of channel use is introduced. Channel use refers to achannel's ability to transmit a sample. For example, if there are tensource samples generated per second and there are thirty channel usesper second, then there are three channel uses available for transmittingeach source sample. If one channel use is available per source sample,the source bandwidth and the channel bandwidth are said to be matched.If the source bandwidth and the channel bandwidth are matched, a linearscaling of the source sample provides an optimal performance.

However, when the source bandwidth and the channel bandwidth are notmatched, there are no known methods that provide a completecharacterization for both worse and better channel conditions,simultaneously. The present disclosure provides a hybrid coding schemefor transmitting on a bandwidth mismatched channel that providesperformance improvement for both worse and better channel conditions.

First consider two other receivers—a receiver with a worse channelcondition and another receiver with a better channel condition. Thereceiver with a worse channel condition has a transmission signal tonoise ratio of SNR⁺ and the receiver with a better channel condition hasa transmission signal to noise ratio of SNR⁻. Hence, SNR⁺≧SNR andSNR⁻≦SNR. The channel output for the channel with the worse channelcondition is denoted by Y⁺. The channel output for the channel with thebetter channel condition is denoted by Y⁻.

For example, if there are three users and the users are designated as agood user, a median user and a bad user, the respective transmissionsignal to noise ratios for the three users may be designated by SNR⁺,SNR, and SNR⁻, respectively.

The bandwidth of the source (source bandwidth) and the bandwidth of thechannel (channel bandwidth) may not be matched. For example, for each msource samples, a total of n channel uses may be available. The ratio ofthe number of channel uses to the number of source samples is referredto as a bandwidth expansion factor. The bandwidth expansion factor isdefined as:

$b\overset{\Delta}{=}{\frac{n}{m}.}$If b<1, the bandwidth is said to be compressed. If b>1, the bandwidth issaid to be expanded. If b=1, the bandwidth is said to be matched.

Using Shannon's source channel separation theorem, while ignoring allother receivers, the optimal distortion for a receiver with transmissionsignal to noise ratio of SNR is given by:

${{D^{*}({SNR})} = {{D( {\frac{n}{m}C} )} = {D({bC})}}},$wherein D(R) is a Gaussian distortion rate function and C is a Gaussianchannel capacity per channel use. The Gaussian channel capacity perchannel use may also be referred to as simply as a Gaussian channelcapacity.

Then, substituting for the Gaussian distortion-rate function and theGaussian channel capacity, the optimal distortion (described above) maybe determined as follows: D*(SNR)=exp(−b log(1+SNR))=(1+SNR)^(−b),wherein the natural logarithm function (log) is used.

For the case in which the bandwidth is expanded (i.e. b>1), a methoddescribed by Reznic, Feder and Zamir may achieve an improved distortionbehavior for either a receiver with worse channel condition or betterchannel condition—but not both.

For example, for the channel conditions SNR⁻, SNR and SNR⁺,respectively, Reznic, Feder and Zamir may achieve either a distortiontriple (D^(†)(SNR⁻), D*(SNR), D*(SNR)) or a distortion triple (D′(SNR⁻),D*(SNR), D^(†)(SNR⁺)), wherein

${{D^{\dagger}( {SNR}^{+} )} = \frac{1}{( {1 + {SNR}} )^{b - 1}( {1 + {SNR}^{+}} )}},{{D^{\dagger}( {SNR}^{-} )} = \frac{1}{( {1 + {SNR}^{-}} )}},{{{and}\mspace{14mu}{D^{\prime}( {SNR}^{-} )}} = {1 - {\frac{{SNR}^{-}}{( {1 + {SNR}} )^{b - 1}( {1 + {SNR}^{-}} )}.}}}$

However, the Reznic, Feder and Zamir method does not achieve improveddistortion behavior for both worse and better channel conditions,simultaneously. Note that D^(†) has greater performance gain over D* ascompared to the performance gain of D′ over the D*. In order to achievethe D^(†)(SNR⁻) for the bad user, Reznic, Feder and Zamir cannot provideany performance gain for the good user over the performance of themedian user. Similarly, in order to achieve D^(†)(SNR⁺) for the gooduser, Reznic, Feder and Zamir can only achieve minimal distortionreduction for the bad user over the median user.

For the case in which the bandwidth is compressed (i.e. b<1), a methoddescribed by Prabhakaran may be used to achieve an improved distortionbehavior for either a receiver with worse channel condition or areceiver with a better channel condition—but not both.

For example, for the channel conditions SNR⁻, SNR and SNR⁺,respectively, Prabhakaran may achieve either a distortion triple(D^(†)(SNR⁻), D*(SNR), D*(SNR)) or a distortion triple (D′(SNR⁻),D*(SNR), D^(†)(SNR⁺)), wherein,

${{D^{\dagger}( {SNR}^{+} )} = {{( {1 - b} ){D^{*}({SNR})}} + {b\frac{SNR}{{( {1 + {SNR}} )^{b}{SNR}^{+}} + {SNR} - {SNR}^{+}}}}},{{D^{\dagger}( {SNR}^{-} )} = {1 - {{bSNR}^{-}\frac{1 + {SNR} - ( {1 + {SNR}} )^{1 - b}}{{SNR}( {1 + {SNR}^{-}} )}}}},{{{and}\mspace{14mu}{D^{\prime}( {SNR}^{-} )}} = {1 - {{bSNR}^{-}\frac{( {1 + {SNR}} )^{b} - 1}{{SNR}( {1 + {SNR}^{-}} )}}}},$but not both.

In both the Reznic, Feder and Zamir and the Prabhakaran approaches(described above), D′(SNR⁻)≧D^(†)(SNR⁻). A coding scheme used for theabove bandwidth expansion and bandwidth compression examples is a codingscheme designed for sending a Gaussian source on a Gaussian channel,wherein the bandwidth is matched (i.e. b=1), and a side information isavailable at the receiver but not at the transmitter. The coding schemeis referred to as a hybrid joint source channel Wyner-Ziv coding scheme.In order to use the hybrid joint source channel Wyner-Ziv coding scheme,first let S_(d) represent the side information, Z represent a Gaussianrandom variable independent of S_(d), and S represent the source. Then,S=S_(d)+Z. The coding scheme then operates as follows:

First, a set of codewords may be generated by an encoder according to adistribution U=X+κS, wherein X is a Gaussian random variable independentof the source S. The objective of the encoder is to find a codewordu^(m) such that the codeword is jointly typical with a source sequenceS^(m). The encoder then transmits u^(m)−κS^(m) on the channel. A decoderreceives a channel output Y^(m). A side information S_(d) ^(m) isavailable at the receiver. The decoder uses Y^(m), S_(d) ^(m) and jointtypicality for decoding. When the rate of the codebook is chosen suchthat I(U; S_(d), Y)>R>I (U;S), then the codeword is correctly decoded.The source estimate is formed using S_(d), U, Y jointly. In order toachieve an optimal performance for the channel condition of SNR anappropriate value is chosen for κ.

It is important to note that other coding schemes may be used. Forexample, an encoder may use a distribution U=X+αT+κS, wherein T is achannel state. The encoder may then transmit the resulting X vector,u^(m)−αT^(m)−κS^(m), on the channel. If the rate of the generated Ucodebook is chosen such that I(U;Y)>R>I(U;T,S), the codeword iscorrectly decoded and the source estimate is formed by using Y, Ujointly.

The present disclosure provides a hybrid coding scheme that achieves thedistortion triple (D^(†)(SNR⁻), D*(SNR), D^(†)(SNR⁺)) for the channelconditions SNR⁻, SNR and SNR⁺, respectively, wherein the bandwidth ofthe channel is expanded.

In one embodiment, the bandwidth expansion factor for the channel has aninteger value. For example, the bandwidth expansion factor,

${b\overset{\Delta}{=}\frac{n}{m}},$for the channel is an integer such that b>1.

In one embodiment, the coding scheme of the present disclosure is basedon recursive quantization. When the bandwidth expansion factor has aninteger value greater than one, the method first breaks the channelbandwidth evenly into b portions, each portion having m channel uses.Each of the resulting portions (having m channel uses) is referred to asa subband of the channel. For example, if there are four source samplesand a total of twelve channel uses, b is then equal to three. Then, themethod breaks the channel bandwidth into three subbands, with each ofthe three subbands having four channel uses.

In one embodiment, the method then digitally quantizes the source with aGaussian codebook. The method then properly scales the quantized sourceto satisfy power constraints with equality. The quantized source isscaled such that the scaled version of the quantized source has a powerlevel that matches the power level of the channel. The method then usesthe resulting scaled and quantized source as the channel input for thefirst subband.

In one embodiment, the method then proceeds to determine the channelinput for the second subband. The channel input for the second subbandis based on the source quantization noise from the coding performed fortransmitting in the first subband. Thus, the method determines thesource quantization noise for the first subband. For example, the methoddetermines the source quantization noise as the difference between thesource and the quantized source derived above for the first subband. Themethod then digitally quantizes the determined source quantization noiseusing a Gaussian codebook. The method then scales the quantized versionto use as the channel input for the second subband.

The method recursively continues until the channel input for the(b−1)-th subband is determined by quantizing and appropriately scalingthe quantization noise from the coding performed for the (b−2)-thsubband.

For the last subband (b-th subband), the method directly scales thequantization noise from the codeword of the (b−1)-th subband and usesthe scaled (uncoded) quantization noise as an input for the lastsubband. The channel input for the last subband is then derived withoutfurther quantization of the quantization noise from the previous (i.e.(b−1)-th) subband.

In one embodiment, the quantization and channel coding rate for each ofthe subbands 1, 2, . . . , b−1 is

$R = {\frac{1}{2}{{\log( {1 + {SNR}} )}.}}$Noting that the Gaussian source is successively refinable, the presentmethod achieves optimal results for the median user and also achievesD^(†)(SNR⁺) for the good user.

In order to illustrate that D^(†)(SNR⁻) is achieved for the bad user,the D(SNR⁻) is derived as follows. The channel quality of SNR⁻ is notsufficient for the codewords of the bad user to be decoded reliably.However, for each subband, the method of the present disclosure is ableto extract information to estimate the source. As described above, eachsubband encodes the quantization noise from the previous subband.

Denoting the quantization noises for the subbands 1, 2, . . . , b−1 byS₁, S₂, . . . , S_(b-1) and their respective quantized versions by Ŝ₁,Ŝ₂, . . . , Ŝ_(b-1), then S=S₁−Ŝ₁+Ŝ₁=S₂+Ŝ₁= . . . =Σ_(i=1) ^(b)Ŝ_(i),wherein S₁=S. For convenience, the method also defines Ŝ_(b)=S_(b).Since Gaussian codebooks are used, the terms Ŝ₁, Ŝ₂, . . . , Ŝ_(b) aremutually independent. The Gaussian sources are successively refinable.Hence, the variance for each S_(i) is given by Var(S_(i))=(1+SNR)^(i-1), for i=1, 2, . . . , b, wherein Var(.) is thevariance of the random variable.

The i-th subband encodes the i-th quantization noise independent ofother noises and subbands. Thus, for the bad user, the channelobservation on the i-th subband is {tilde over (S)}_(i). The estimate ofŜ_(i) may then be formed as: S(SNR⁻)=Σ_(i=1) ^(b)

(Ŝ_(i)|{tilde over (S)}_(i)).

Then, D(SNR⁻)=

(S−S(SNR⁻))²=Σ_(i=1) ^(b)

[Ŝ_(i)−

(Ŝ_(i)|{tilde over (S)}_(i))]². Since a Gaussian codebook is used, thechoice of the channel coding rates gives:

${{I( {S_{i};{\hat{S}}_{i}} )} = {{\frac{1}{2}{\log( {1 + {SNR}} )}\mspace{14mu}{for}\mspace{14mu} i} = 1}},2,\ldots\mspace{11mu},{b - 1.}$Then, for the variances,

${{{Var}( {\hat{S}}_{i} )} = {{\frac{SNR}{( {1 + {SNR}} )^{i}}\mspace{14mu}{for}\mspace{14mu} i} = 1}},2,\ldots\mspace{11mu},{b - 1.}$

Then, Ŝ_(i) may be taken as a fictitious source that is transmitted on abandwidth matched system un-coded. Thus,

${{{??}\lbrack {{\hat{S}}_{i} - {{??}( {{\hat{S}}_{i}❘{\hat{S}}_{i}} )}} \rbrack}^{2} = {{{{Var}( {\hat{S}}_{i} )}\frac{1}{1 + {SNR}^{-}}} = \frac{SNR}{( {1 + {SNR}} )^{i}( {1 + {SNR}^{-}} )}}},{{{for}\mspace{14mu} i} = 1},2,\ldots\mspace{11mu},{b - 1.}$

For the last subband, Ŝ_(b)=S_(b), and

${{??}\lbrack {{\hat{S}}_{b} - {{??}( {{\hat{S}}_{b}❘{\hat{S}}_{b}} )}} \rbrack}^{2} = {{{{Var}( {\hat{S}}_{b} )}\frac{1}{1 + {SNR}^{-}}} = {\frac{1}{( {1 + {SNR}} )^{b - 1}( {1 + {SNR}^{-}} )}.}}$

Combining the above results,

${{D( {SNR}^{-} )} = {( {\sum\limits_{i = 1}^{b}\;\frac{SNR}{( {1 + {SNR}} )^{i}( {1 + {SNR}^{-}} )}} ) + {\frac{1}{( {1 + {SNR}} )^{b - 1}( {1 + {SNR}^{-}} )}.\mspace{14mu}{Then}}}},{{D( {SNR}^{-} )} = {\frac{1}{1 + {SNR}^{-}} = {{D^{\dagger}( {SNR}^{-} )}.}}}$Thus, for the integer bandwidth expansion case, the distortion triple(D^(†)(SNR⁻), D*(SNR), D^(†)(SNR⁺)) for the channel conditions SNR⁻, SNRand SNR⁺, respectively, is achieved.

In another embodiment, the bandwidth expansion factor may havefractional value. If the bandwidth expansion factor has a fractionalvalue, the method may encode for transmission on the integer portions ofthe channel uses as described above. In one embodiment, for thefractional portion of the channel use, the method may perform digitalcompression to fit the sample in the fractional portion of the channeluse. In one embodiment, for the fractional portion of the channel use,the method may simply not transmit the source. For example, if there are2.2 channel uses for each source sample, the method may transmit on twochannel uses and ignore the 0.2 portion. In another example, the methodmay send a compressed version of the sample on the remaining bandwidth(on the remaining 0.2 channel uses). It should be noted that there maybe other encoding techniques that can be used to fill the fractionalportions of the leftover channel bandwidth.

FIG. 2 illustrates a flow chart of a method 200 for providingtransmission of data on a channel in a wireless network, wherein a ratioof a number of channel uses of the channel to a number of source samplesis an integer value, e.g., greater than one. In one embodiment, one ormore steps of method 200 are implemented by an encoder used in atransmission system, e.g., a transmission system used in a wirelessnetwork. Method 200 starts in step 205 and proceeds to step 210.

In step 210, method 200 determines a ratio of the number of channel usesof the channel to the number of source samples. For example, for aparticular channel, if the number of source samples is m and the numberof channel uses is n, the ratio of the number of channel uses of thechannel to the number of source samples is determined as:

$b\overset{\Delta}{=}{\frac{n}{m}.}$

In step 213, method 200 breaks or divides the channel bandwidth into aplurality of subbands of equal bandwidth. For example, the method breaksthe channel bandwidth in accordance with the ratio of the number ofchannel uses of the channel to the number of source samples as describedabove, i.e., into b subbands of equal bandwidth, with each of the bsubbands comprising m channel uses. For example, if m=4 and n=12, thenb=3. Then, the channel bandwidth of the particular channel is evenlydivided into 3 subbands, with each subband comprising 4 channel uses.The method then proceeds to determine the channel input for each of theplurality of subbands.

In step 215, method 200 receives a source sample block. For example,method 200 receives source sample block comprising m source samples tobe encoded and transmitted across the network.

In step 217, method 200 determines the channel input for each of theplurality of subbands from the source sample block in accordance with ahybrid coding scheme. For example, for each of the subbands 1, 2, . . ., b, the method determines the channel input recursively in accordancewith the hybrid analog and digital coding scheme of the presentdisclosure. In one embodiment, FIG. 3 illustrates a flowchart of amethod for determining of the channel input for each of the plurality ofsubbands in accordance with the hybrid coding scheme.

Note that the channel input for the first subband is based on quantizingthe source sample block. The channel input for the second subband isbased on quantizing the quantization noise from the coding performed fortransmitting in the first subband. The channel input for the thirdsubband is based on quantizing the quantization noise from the codingperformed for transmitting in the second subband. The process continuesuntil the channel input for the (b−1)-th subband is determined byquantizing the quantization noise from the coding performed for the(b−2)-th subband. The channel input for the last subband is determinedfrom the quantization noise from the coding performed for the (b−1)-thsubband without further quantization. That is, for the last subband, thechannel input is determined by scaling the quantization noise of theprevious subband without performing further quantization. It should benoted that scaling is performed to satisfy a power constraint withequality.

In step 219, method 200 transmits the channel inputs that are determinedfor each of the plurality of subbands over the channel. For example, thechannel inputs determined in step 217 for the subbands 1, 2, . . . , bare transmitted towards a decoder over the network. The decoder may thenreconstruct the source sample block. For example, the decoder may use alinear mean square estimation technique for reconstructing the sourcesample block. The method either proceeds to step 215 to continuereceiving source sample blocks or to step 222 to end the currentprocess.

FIG. 3 illustrates a flowchart of a method 300 for determining of achannel input for each of a plurality of subbands in accordance with thehybrid coding scheme. For example, the method 300 may be used in step217 of FIG. 2 to recursively determine the channel input for each of theplurality of subbands. Method 300 starts in step 303 and proceeds tostep 325.

In step 325, method 300 determines if the channel input being determinedis a channel input for a first subband of a plurality of subbands. Ifthe channel input being determined is for the first subband, the methodproceeds to step 327. Otherwise, the method proceeds to step 360.

In step 327, method 300 quantizes a source sample with a Gaussiancodebook. For example, the method quantizes an analog source sample toobtain a digital quantized source sample using a Gaussian codebook.

In step 330, method 300 scales the source sample that is quantized. Inone embodiment, the scaling of the source sample that is quantized isperformed by matching a power level of the source sample that isquantized to a power level of the channel. For example, the sourcesample that is quantized is scaled such that the scaled version of thesource sample that is quantized has a power level that matches the powerlevel of the channel.

In step 340, method 300 output the source sample that is quantized andscaled as the channel input for the first subband of the plurality ofsubbands. The channel input for the first subband may then betransmitted as the channel input over the network, as described in step219 of FIG. 2.

In step 350, method 300 determines a quantization noise for the firstsubband of the plurality of subbands. For example, the method determinesthe source quantization noise for the first subband as the differencebetween the source sample and the source sample that is quantized (e.g.,as derived above in step 327). The method proceeds to step 325.

In step 360, method 300 determines if the channel input being determinedis a channel input of a last subband. Thus, steps 325 and 360effectively determine a position of a particular subband relative to theplurality of subbands. If the channel input being determined is that ofthe last subband, the method proceeds to step 385. Otherwise, the methodproceeds to step 365. For example, if the channel input being determinedis for the b-th subband, the method proceeds to step 385. If the channelinput being determined is for the subbands 2, 3, . . . , b−1, the methodproceeds to step 365.

In step 365, method 300 quantizes the quantization noise, wherein thequantization noise is for a coding performed for a subband previous tothe subband for which the channel input is being determined. Forexample, in order to determine the channel input for the second subband,the method quantizes the quantization noise for the coding performed forthe first subband using a Gaussian codebook. In another example, if thechannel input being determined is the channel input for the (b−1)-thsubband, the method quantizes the quantization noise from the codingperformed for the (b−2)-th subband.

In step 370, method 300 scales the quantization noise that is quantized,wherein the quantization noise is for a coding performed for a subbandprevious to the subband for which the channel input is being determined.For example, the quantization noise that is quantized in step 365 isscaled such that the power level matches the power level of the channel.

In step 375, method 300 outputs the quantization noise that is quantizedand scaled as the channel input for the subband for which the channelinput is being determined. The channel input that is outputted may thenbe transmitted on the particular subband over the network, as describedin step 219 of FIG. 2.

In step 377, method 300 determines a quantization noise for the subbandfor which the channel input is being determined (i.e., the currentsubband). For example, the method determines the quantization noise forthe current subband as the difference between the quantization noise forcoding performed for the subband previous to the current subband and thequantization noise that is quantized, as described above in step 365.For example, the quantization noise for the 3^(rd) subband is determinedas follows: first, the coding performed for transmission on the 2^(nd)subband is retrieved. The retrieved quantization noise is quantized asdescribed in step 365. Then, the difference between the quantizationnoise that is retrieved and the quantized version is determined. Thedifference that is determined is then the quantization noise for the3^(rd) subband. The method proceeds to step 325 where the process isperformed iteratively for a plurality of subbands.

In step 385, method 300 scales the quantization noise, wherein thequantization noise is for a coding performed for a subband previous tothe subband for which the channel input is being determined. For thelast subband (b-th subband), the method directly scales the quantizationnoise from the codeword of the (b−1)-th subband and uses the scaledquantization noise as an input for the last subband, without furtherquantizing.

In step 390, method 300 outputs the quantization noise that is scaled asthe channel input for the subband for which the channel input is beingdetermined. For example, the method outputs the scaled version of thequantization noise for the coding performed for transmission on the(b−1)-th subband as the channel input for the last subband (b-thsubband). The channel input that is outputted may then be transmitted asthe channel input for the last subband over the network, as described instep 219 of FIG. 2. The method either proceeds to step 325 to continuedetermining channel inputs or to step 395 to end the current process.

Note that in one embodiment, an encoder and a decoder are used at bothlocations. For example, the transmission system of FIG. 1 may be abidirectional transmission system. In one embodiment, the encoder anddecoder may be integrated in one device.

It should be noted that although not explicitly specified, one or moresteps of the methods 200 or 300 described herein may include a storing,displaying and/or outputting step as required for a particularapplication. In other words, any data, records, fields, and/orintermediate results discussed in the method can be stored, displayed,and/or outputted to another device as required for a particularapplication. Furthermore, steps or blocks in FIG. 2 or FIG. 3 thatrecite a determining operation, or involve a decision, do notnecessarily require that both branches of the determining operation bepracticed. In other words, one of the branches of the determiningoperation can be deemed as an optional step.

FIG. 4 depicts a high level block diagram of a general purpose computersuitable for use in performing the functions described herein. Asdepicted in FIG. 4, the system 400 comprises a hardware processorelement 402 (e.g., a CPU), a memory 404, e.g., random access memory(RAM) and/or read only memory (ROM), a module 405 for providingtransmission of data on a bandwidth mismatched channel, and variousinput/output devices 406 (e.g., storage devices, including but notlimited to, a tape drive, a floppy drive, a hard disk drive or a compactdisk drive, a receiver, a transmitter, a speaker, a display, a speechsynthesizer, an output port, and a user input device (such as akeyboard, a keypad, a mouse, and the like)).

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a general purposecomputer or any other hardware equivalents, e.g., computer readableinstructions pertaining to the method(s) discussed above can be used toconfigure a hardware processor to perform the steps of the abovedisclosed method. In one embodiment, the present module or process 405for providing transmission of data on a bandwidth mismatched channel canbe loaded into memory 404 and executed by hardware processor 402 toimplement the functions as discussed above. As such, the present process405 for providing transmission of data on a bandwidth mismatched channel(including associated data structures) of the present disclosure can bestored on a non-transitory (e.g., tangible and physical) computerreadable storage medium, e.g., RAM memory, magnetic or optical drive ordiskette and the like. For example, the processor 402 can be programmedor configured with instructions (e.g., computer readable instructions)to perform the steps of methods 200 and 300.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method for transmitting data on a channel in anetwork, comprising: determining, by a processor, a ratio of a number ofchannel uses of the channel to a number of source samples, wherein thenumber of channel uses refers to an ability of the channel to transmitthe source samples; dividing, by the processor, a channel bandwidth intoa plurality of subbands of equal bandwidth in accordance with the ratio;receiving, by the processor, a source sample block; determining, by theprocessor, a channel input for each of the plurality of subbands fromthe source sample block in accordance with a hybrid coding scheme; andtransmitting, by the processor, for each of the plurality of subbands,the channel input that is determined over the network to a decoder thatreconstructs the source sample block using the channel input for theeach of the plurality of subbands regardless of whether a channelcondition of a respective subband of the plurality of subbands is worseor better than a target channel condition.
 2. The method of claim 1,wherein the ratio is a bandwidth expansion factor that has an integervalue that is greater than one.
 3. The method of claim 1, wherein thechannel input for each of the plurality of subbands is scaled such thata power level of the channel input matches a power level of the channel.4. The method of claim 1, wherein the determining the channel input fora particular subband of the plurality of subbands, comprises:determining a position of a particular subband relative to the pluralityof subbands.
 5. The method of claim 4, wherein the determining thechannel input for the particular subband, further comprises: quantizinga source sample associated with the particular subband with a codebook,if the particular subband is a first subband of the plurality ofsubbands; scaling the source sample that is quantized, when theparticular subband is the first subband; outputting the source samplethat is quantized and scaled as the channel input for the first subbandof the plurality of subbands; and determining a quantization noise forthe first subband of the plurality of subbands.
 6. The method of claim4, wherein the determining the channel input for the particular subband,further comprises: quantizing a quantization noise, wherein thequantization noise is for a coding performed for a subband previous tothe particular subband for which the channel input is being determined,when the particular subband is not the first subband of the plurality ofsubbands and the particular subband is not the last subband of theplurality of subbands; scaling the quantization noise that is quantized,when the particular subband is not the first subband of the plurality ofsubbands and the particular subband is not the last subband of theplurality of subbands; outputting the quantization noise that isquantized and scaled as the channel input for the particular subband forwhich the channel input is being determined; and determining aquantization noise for the particular subband for which the channelinput is being determined.
 7. The method of claim 4, wherein thedetermining the channel input for the particular subband, furthercomprises: scaling a quantization noise, wherein the quantization noiseis for a coding performed for a subband previous to a last subband ofthe plurality of subbands, when the particular subband is the lastsubband of the plurality of subbands; and outputting the quantizationnoise that is scaled as the channel input for the last subband of theplurality of subbands.
 8. A non-transitory computer-readable mediumstoring a plurality of instructions, which when executed by a processor,cause the processor to perform operations for transmitting data on achannel in a network, the operations comprising: determining a ratio ofa number of channel uses of the channel to a number of source samples,wherein the number of channel uses refers to an ability of the channelto transmit the source samples; dividing a channel bandwidth into aplurality of subbands of equal bandwidth in accordance with the ratio;receiving a source sample block; determining a channel input for each ofthe plurality of subbands from the source sample block in accordancewith a hybrid coding scheme; and transmitting, for each of the pluralityof subbands, the channel input that is determined over the network to adecoder that reconstructs the source sample block using the channelinput for the each of the plurality of subbands regardless of whether achannel condition of a respective subband of the plurality of subbandsis worse or better than a target channel condition.
 9. Thenon-transitory computer-readable medium of claim 8, wherein the ratio isa bandwidth expansion factor that has an integer value that is greaterthan one.
 10. The non-transitory computer-readable medium of claim 8,wherein the channel input for each of the plurality of subbands isscaled such that a power level of the channel input matches a powerlevel of the channel.
 11. The non-transitory computer-readable medium ofclaim 8, wherein the determining the channel input for a particularsubband of the plurality of subbands, comprises: determining a positionof a particular subband relative to the plurality of subbands.
 12. Thenon-transitory computer-readable medium of claim 11, wherein thedetermining the channel input for the particular subband, furthercomprises: quantizing a source sample associated with the particularsubband with a codebook, if the particular subband is a first subband ofthe plurality of subbands; scaling the source sample that is quantized,when the particular subband is the first subband; outputting the sourcesample that is quantized and scaled as the channel input for the firstsubband of the plurality of subbands; and determining a quantizationnoise for the first subband of the plurality of subbands.
 13. Thenon-transitory computer-readable medium of claim 11, wherein thedetermining the channel input for the particular subband, furthercomprises: quantizing a quantization noise, wherein the quantizationnoise is for a coding performed for a subband previous to the particularsubband for which the channel input is being determined, when theparticular subband is not the first subband of the plurality of subbandsand the particular subband is not the last subband of the plurality ofsubbands; scaling the quantization noise that is quantized, when theparticular subband is not the first subband of the plurality of subbandsand the particular subband is not the last subband of the plurality ofsubbands; outputting the quantization noise that is quantized and scaledas the channel input for the particular subband for which the channelinput is being determined; and determining a quantization noise for theparticular subband for which the channel input is being determined. 14.The non-transitory computer-readable medium of claim 11, wherein thedetermining the channel input for the particular subband, furthercomprises: scaling a quantization noise, wherein the quantization noiseis for a coding performed for a subband previous to a last subband ofthe plurality of subbands, when the particular subband is the lastsubband of the plurality of subbands; and outputting the quantizationnoise that is scaled as the channel input for the last subband of theplurality of subbands.
 15. An apparatus for providing transmission ofdata on a channel in a network, comprising: a processor; and acomputer-readable medium storing a plurality of instructions which, whenexecuted by the processor, cause the processor to perform operations,the operations comprising: determining a ratio of a number of channeluses of the channel to a number of source samples, wherein the number ofchannel uses refers to an ability of the channel to transmit the sourcesamples; dividing a channel bandwidth into a plurality of subbands ofequal bandwidth in accordance with the ratio; receiving a source sampleblock; determining a channel input for each of the plurality of subbandsfrom the source sample block in accordance with a hybrid coding scheme;and transmitting, for each of the plurality of subbands, the channelinput that is determined over the network to a decoder that reconstructsthe source sample block using the channel input for the each of theplurality of subbands regardless of whether a channel condition of arespective subband of the plurality of subbands is worse or better thana target channel condition.
 16. The apparatus of claim 15, wherein thefactor has an integer value that is greater than one.
 17. The apparatusof claim 15, wherein the channel input for each of the plurality ofsubbands is scaled such that a power level of the channel input matchesa power level of the channel.
 18. The apparatus of claim 15, wherein thedetermining the channel input for a particular subband of the pluralityof subbands, comprises: determining a position of a particular subbandrelative to the plurality of subbands.
 19. The apparatus of claim 18,wherein the determining the channel input for the particular subband,further comprises: quantizing a source sample associated with theparticular subband with a codebook, if the particular subband is a firstsubband of the plurality of subbands; scaling the source sample that isquantized, when the particular subband is the first subband; outputtingthe source sample that is quantized and scaled as the channel input forthe first subband of the plurality of subbands; and determining aquantization noise for the first subband of the plurality of subbands.20. The apparatus of claim 18, wherein the determining the channel inputfor the particular subband, further comprises: quantizing a quantizationnoise, wherein the quantization noise is for a coding performed for asubband previous to the particular subband for which the channel inputis being determined, when the particular subband is not the firstsubband of the plurality of subbands and the particular subband is notthe last subband of the plurality of subbands; scaling the quantizationnoise that is quantized, when the particular subband is not the firstsubband of the plurality of subbands and the particular subband is notthe last subband of the plurality of subbands; outputting thequantization noise that is quantized and scaled as the channel input forthe particular subband for which the channel input is being determined;and determining a quantization noise for the particular subband forwhich the channel input is being determined.